projects/ML_StudentPerformance/app.py

from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipelines.predict_pipeline import CustomData, PredictPipeline

application = Flask(__name__)

app = application

# Route for home page
@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predictdata',methods=['GET','POST'])
def predict_datapoint():
    if request.method == 'GET':
        return render_template('home.html')
    else: 
        data = CustomData(
            gender=request.form.get('gender'),
            race_ethnicity=request.form.get('ethnicity'),
            parental_level_of_education=request.form.get('parental_level_of_education'),
            lunch=request.form.get('lunch'),
            test_preparation_course=request.form.get('test_preparation_course'),
            reading_score=float(request. form. get('writing_score')),
            writing_score=float(request. form.get('reading_score'))
        )
        pred_df = data.get_data_as_dataframe()
        print(pred_df)

        predict_pipeline = PredictPipeline()
        results = predict_pipeline.predict(pred_df)

        return render_template('home.html',results = results[0])

if __name__=='__main__':
    app.run('0.0.0.0')